SoroushVahidi/scidocs huggingface dataset:
The evolution of human skin coloration.
(2000)

Skin color is one of the most conspicuous ways in which humans vary and has been widely used to define human races. Here we present new evidence indicating that variations in skin color are adaptive, and are related to the regulation of ultraviolet (UV) radiation penetration in the integument and its direct and indirect effects on fitness. Using remotely sensed data on UV radiation levels, hypotheses concerning the distribution of the skin colors of indigenous peoples relative to UV levels were tested quantitatively in this study for the first time. The major results of this study are: (1) skin reflectance is strongly correlated with absolute latitude and UV radiation levels. The highest correlation between skin reflectance and UV levels was observed at 545 nm, near the absorption maximum for oxyhemoglobin, suggesting that the main role of melanin pigmentation in humans is regulation of the effects of UV radiation on the contents of cutaneous blood vessels located in the dermis. (2) Predicted skin reflectances deviated little from observed values. (3) In all populations for which skin reflectance data were available for males and females, females were found to be lighter skinned than males. (4) The clinal gradation of skin coloration observed among indigenous peoples is correlated with UV radiation levels and represents a compromise solution to the conflicting physiological requirements of photoprotection and vitamin D synthesis. The earliest members of the hominid lineage probably had a mostly unpigmented or lightly pigmented integument covered with dark black hair, similar to that of the modern chimpanzee. The evolution of a naked, darkly pigmented integument occurred early in the evolution of the genus Homo. A dark epidermis protected sweat glands from UV-induced injury, thus insuring the integrity of somatic thermoregulation. Of greater significance to individual reproductive success was that highly melanized skin protected against UV-induced photolysis of folate (Branda & Eaton, 1978, Science201, 625-626; Jablonski, 1992, Proc. Australas. Soc. Hum. Biol.5, 455-462, 1999, Med. Hypotheses52, 581-582), a metabolite essential for normal development of the embryonic neural tube (Bower & Stanley, 1989, The Medical Journal of Australia150, 613-619; Medical Research Council Vitamin Research Group, 1991, The Lancet338, 31-37) and spermatogenesis (Cosentino et al., 1990, Proc. Natn. Acad. Sci. U.S.A.87, 1431-1435; Mathur et al., 1977, Fertility Sterility28, 1356-1360).As hominids migrated outside of the tropics, varying degrees of depigmentation evolved in order to permit UVB-induced synthesis of previtamin D(3). The lighter color of female skin may be required to permit synthesis of the relatively higher amounts of vitamin D(3)necessary during pregnancy and lactation. Skin coloration in humans is adaptive and labile. Skin pigmentation levels have changed more than once in human evolution. Because of this, skin coloration is of no value in determining phylogenetic relationships among modern human groups.
Robust and false positive free watermarking in IWT domain using SVD and ABC
(2016)

Watermarking is used to protect the copyrighted materials from being misused and help us to know the lawful ownership. The security of any watermarking scheme is always a prime concern for the developer. In this work, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored. Generally, SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authenticating the wrong owner. We are proposing a novel solution to this false positive problem; that arises in SVD based approach. Firstly, IWT is employed on the host image and then SVD is performed on this transformed host. The properties of IWT and SVD help in achieving high value of robustness. Singular values are used for the watermark embedding. In order to further improve the quality of watermarking, the optimization of scaling factor (mixing ratio) is performed with the help of artificial bee colony (ABC) algorithm. A comparison with other schemes is performed to show the superiority of proposed scheme. & 2015 Elsevier Ltd. All rights reserved.
A Literature Survey of Benchmark Functions For Global Optimization Problems
(2013)

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.
Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems
(2018)

In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.
A low-voltage, low quiescent current, low drop-out regulator
(1998)

The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation
(2018)

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor checkins, which are essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins. Specifically, one time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.
Absorptive Frequency-Selective Transmission Structure With Square-Loop Hybrid Resonator
(2017)

A novel design of an absorptive frequency-selective transmission structure (AFST) is proposed. This structure is based on the design of a frequency-dependent lossy layer with square-loop hybrid resonator (SLHR). The parallel resonance provided by the hybrid resonator is utilized to bypass the lossy path and improve the insertion loss. Meanwhile, the series resonance of the hybrid resonator is used for expanding the upper absorption bandwidth. Furthermore, the absorption for out-of-band frequencies is achieved by using four metallic strips with lumped resistors, which are connected with the SLHR. The quantity of lumped elements required in a unit cell can be reduced by at least 50% compared to previous structures. The design guidelines are explained with the aid of an equivalent circuit model. Both simulation and experiment results are presented to demonstrate the performance of our AFST. It is shown that an insertion loss of 0.29 dB at 6.1 GHz and a 112.4% 10 dB reflection reduction bandwidth are obtained under the normal incidence.
Implementation of AES-GCM encryption algorithm for high performance and low power architecture Using FPGA
(2015)

Evaluation of the Advanced Encryption Standard (AES) algorithm in FPGA is proposed here. This Evaluation is compared with other works to show the efficiency. Here we are concerned about two major purposes. The first is to define some of the terms and concepts behind basic cryptographic methods, and to offer a way to compare the myriad cryptographic schemes in use today. The second is to provide some real examples of cryptography in use today. The design uses an iterative looping approach with block and key size of 128 bits, lookup table implementation of S-box. This gives low complexity architecture and easily achieves low latency as well as high throughput. Simulation results, performance results are presented and compared with previous reported designs. Since its acceptance as the adopted symmetric-key algorithm, the Advanced Encryption Standard (AES) and its recently standardized authentication Galois/Counter Mode (GCM) have been utilized in various security-constrained applications. Many of the AES-GCM applications are power and resource constrained and requires efficient hardware implementations. In this project, AES-GCM algorithms are evaluated and optimized to identify the high-performance and low-power architectures. The Advanced Encryption Standard (AES) is a specification for the encryption of electronic data. The Cipher Block Chaining (CBC) mode is a confidentiality mode whose encryption process features the combining (“chaining”) of the plaintext blocks with the previous Cipher text blocks. The CBC mode requires an IV to combine with the first plaintext block. The IV need not be secret, but it must be unpredictable. Also, the integrity of the IV should be protected. Galois/Counter Mode (GCM) is a block cipher mode of operation that uses universal hashing over a binary Galois field to provide authenticated encryption. Galois Hash is used for authentication, and the Advanced Encryption Standard (AES) block cipher is used for encryption in counter mode of operation. To obtain the least-complexity S-box, the formulations for the Galois Field (GF) sub-field inversions in GF (24) are optimized By conducting exhaustive simulations for the input transitions, we analyze the synthesis of the AES S-boxes considering the switching activities, gate-level net lists, and parasitic information. Finally, by implementation of AES-GCM the high-performance GF (2128) multiplier architectures, gives the detailed information of its performance. An optimized coding for the implementation of Advanced Encryption Standard-Galois Counter Mode has been developed. The speed factor of the algorithm implementation has been targeted and a software code in Verilog HDL has been developed. This implementation is useful in wireless security like military communication and mobile telephony where there is a grayer emphasis on the speed of communication.
Multi-Document Summarization using Sentence-based Topic Models
(2009)

Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.
Intrusion detection by machine learning: A review
(2009)

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided. 2009 Elsevier Ltd. All rights reserved.
Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments
(2014)

Software Defined Networks (SDNs) based on the OpenFlow (OF) protocol export controlplane programmability of switched substrates. As a result, rich functionality in traffic management, load balancing, routing, firewall configuration, etc. that may pertain to specific flows they control, may be easily developed. In this paper we extend these functionalities with an efficient and scalable mechanism for performing anomaly detection and mitigation in SDN architectures. Flow statistics may reveal anomalies triggered by large scale malicious events (typically massive Distributed Denial of Service attacks) and subsequently assist networked resource owners/operators to raise mitigation policies against these threats. First, we demonstrate that OF statistics collection and processing overloads the centralized control plane, introducing scalability issues. Second, we propose a modular architecture for the separation of the data collection process from the SDN control plane with the employment of sFlow monitoring data. We then report experimental results that compare its performance against native OF approaches that use standard flow table statistics. Both alternatives are evaluated using an entropy-based method on high volume real network traffic data collected from a university campus network. The packet traces were fed to hardware and software OF devices in order to assess flow-based datagathering and related anomaly detection options. We subsequently present experimental results that demonstrate the effectiveness of the proposed sFlow-based mechanism compared to the native OF approach, in terms of overhead imposed on usage of system resources. Finally, we conclude by demonstrating that once a network anomaly is detected and identified, the OF protocol can effectively mitigate it via flow table modifications. 2013 Elsevier B.V. All rights reserved.
Software-Defined Networking: A Comprehensive Survey
(2014)

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network’s control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms with a focus on aspects such as resiliency, scalability, performance, security, and dependabilityVas well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined
Revisiting Traffic Anomaly Detection Using Software Defined Networking
(2011)

Despite their exponential growth, home and small office/home office networks continue to be poorly managed. Consequently, security of hosts in most home networks is easily compromised and these hosts are in turn used for largescale malicious activities without the home users’ knowledge. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and contain network security problems in home and home office networks. We show how four prominent traffic anomaly detection algorithms can be implemented in an SDN context using Openflow compliant switches and NOX as a controller. Our experiments indicate that these algorithms are significantly more accurate in identifying malicious activities in the home networks as compared to the ISP. Furthermore, the efficiency analysis of our SDN implementations on a programmable home network router indicates that the anomaly detectors can operate at line rates without introducing any performance penalties for the home network traffic.
A clean slate 4D approach to network control and management
(2005)

Today's data networks are surprisingly fragile and difficult to manage. We argue that the root of these problems lies in the complexity of the control and management planes--the software and protocols coordinating network elements--and particularly the way the decision logic and the distributed-systems issues are inexorably intertwined. We advocate a complete refactoring of the functionality and propose three key principles--network-level objectives, network-wide views, and direct control--that we believe should underlie a new architecture. Following these principles, we identify an extreme design point that we call "4D," after the architecture's four planes: decision, dissemination, discovery, and data. The 4D architecture completely separates an AS's decision logic from pro-tocols that govern the interaction among network elements. The AS-level objectives are specified in the decision plane, and en-forced through direct configuration of the state that drives how the data plane forwards packets. In the 4D architecture, the routers and switches simply forward packets at the behest of the decision plane, and collect measurement data to aid the decision plane in controlling the network. Although 4D would involve substantial changes to today's control and management planes, the format of data packets does not need to change; this eases the deployment path for the 4D architecture, while still enabling substantial innovation in network control and management. We hope that exploring an extreme design point will help focus the attention of the research and industrial communities on this crucially important and intellectually challenging area.
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
(2017)

Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithmby considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes. ARTICLE HISTORY Received 21 March 2016 Accepted 28 September 2016